AightBits

This post is a simplified walkthrough of how Large Language Models (LLMs) are trained, evaluated, and deployed. It’s written for people who are interested in AI and want a general understanding of the process without diving into the math or implementation details. While some concepts have been simplified, the focus is on staying accurate and technically grounded. The goal is to explain the lifecycle of a model like ChatGPT, Claude, and DeepSeek in a step-by-step way, from early design decisions to eventual retirement.

1) Architecture and Design

The first step in creating a transformer model is designing its architecture. This involves decisions like how many layers the model will have, how large each layer should be, and how much context it can process in a single pass. These choices affect the model’s performance, cost, and overall ability to handle different tasks. Once these parameters are set, the architecture is locked in and defines the model for the rest of its lifecycle.

At this point, the model is an empty, uninitialized container. See Figure 1, Step 1.

2) Data Curation

Next, the model needs data to learn from. A large corpus of text is gathered — books, websites, technical documents, and other diverse sources. However, not all text is useful. Data needs to be cleaned, filtered, and balanced to ensure it’s high quality. Redundant or irrelevant content is removed, and efforts are made to ensure a good variety of topics and writing styles. The data teaches the model language patterns, but its quality at this stage directly impacts the model’s eventual performance.

3) Initialization

Before training can begin, the model must be initialized. This means assigning starting values to all of its internal parameters — the numerical weights that determine how it processes input. These values are typically set using random distributions within carefully chosen ranges.

The goal is to avoid patterns that would interfere with learning while still giving the model a consistent structure to build on. At this point, the model has no knowledge or skill. It can technically produce output, but the output is random and meaningless. Initialization simply prepares the model to begin learning during pretraining.

Now the model is full of random values. See Figure 1, Step 2.

4) Pretraining

Pretraining is where the core learning happens. The model processes billions or even trillions of words, learning to predict the next token in a sentence based on the context it has seen. It doesn’t learn facts in the traditional sense, but instead develops an understanding of language patterns that encapsulates knowledge in training data.

More specifically, training data is broken into chunks and the randomly initialized weights are adjusted like a massive number of interconnected knobs and dials until the new model can predict (complete) parts of training data accurately enough on its own — a process that uses an algorithm called backpropagation to calculate gradients and then optimization algorithm that uses those gradients to calculate the model’s weights.

This stage is computationally expensive and can take weeks to months, depending on the size of the model and available computing resources. When it’s finished, the model can generate coherent text but is still not capable of performing specific tasks well.

As shown in Figure 1, Step 3, the model can now predict text, but without guidance. If you ask a pretrained model, “What is a cat?” it might answer, “What is a dog? What is a horse?” It still needs further training to be a useful tool.

5) Testing

After pretraining, the model is tested to understand its general language ability. This involves running it through a variety of evaluation tasks that measure how well it can read, write, and reason in natural language. At this point, the model has not been trained to follow instructions or respond in a conversational format. The goal is to see what it can do based only on the patterns it picked up during pretraining.

To test it, the model is given sample inputs and asked to generate outputs. These outputs are compared to expected results, or reviewed by humans to check for clarity, relevance, and basic reasoning.

Testing looks for signs that the model has developed a strong grasp of grammar, sentence structure, and basic logic. It also helps identify where the model is weak, such as producing inconsistent answers or failing to stay on topic. The results help determine whether the model is ready for fine-tuning or if adjustments to training or data are needed first.

6) Fine-Tuning

Fine-tuning is where the model is adapted to specific tasks. After pretraining, it’s trained on smaller, task-specific datasets to improve its performance for those tasks. For example, if you want the model to answer questions in a specific field like law or medicine, it will be fine-tuned on relevant documents from those areas. This step helps the model take the general language skills it learned during pretraining and apply them to real-world tasks.

There are many methodologies for fine-tuning a model, and these are the most common:

• Domain- and function-specific fine-tuning: The most common method of fine‑tuning involves adjusting the model’s learned language patterns, or weights, using a much smaller but more tightly focused, manually constructed dataset that reflects the developer’s desired function (chat, instruction‑following), style (formal, casual), domain knowledge (legal, medical), and behavior (tool‑calling, refusal of inappropriate requests).
• Supervised Fine-Tuning (SFT): This method fine‑tunes a model using a large, well‑labeled collection of example prompts paired with ideal responses. The goal is not just to adjust language patterns but to explicitly teach the model how to respond to given inputs in a consistent way. SFT can be applied to a model directly after pretraining or to one that has already been fine‑tuned by other means. While domain- and function-specific fine‑tuning shapes the model’s general language use through a focused dataset, SFT trains it to follow a structured set of instructions and to produce the exact type of output the developer wants.
• Reinforcement Learning from Human Feedback (RLHF): This method builds on supervised fine‑tuning by using evaluations from trained human reviewers to further guide the model’s behavior. These reviewers compare different possible answers to the same promvpt and indicate which they prefer. The model then learns from these preferences to produce responses that are more likely to match what reviewers judge as helpful, accurate, and appropriate. This method is especially useful for improving a model’s ability to follow instructions, avoid unwanted behaviors, and respond in a way that feels natural to its intended audience.
• Direct Preference Optimization (DPO): DPO is an alternative to RLHF that also learns from human preferences but removes the extra “reward model” step. Instead of first building a separate model to score answers, DPO directly adjusts the language model’s behavior using the preference data itself. Trained reviewers still compare pairs of answers and indicate which they prefer, but the model is updated in a more direct and efficient way. This often makes DPO simpler to apply than RLHF while still steering the model toward producing answers that match what reviewers find most useful and appropriate.

Now that fine-tuning is complete, we can ask our question again: “What is a cat?” For a chat or instruction-tuned model, which is fine-tuned with data including questions and answers and conversations, the model should now respond with a more appropriate answer, i.e., “A cat is a furry mammal.” See Figure 1, Step 4.

7) Further Testing

After improving the model’s task-specific language ability, the goal is to evaluate how well the model performs specific tasks and how closely it follows instructions. At this stage, testing looks at whether the model responds in ways that are useful, accurate, and appropriate for the intended use.

The process involves giving the model a wide range of example prompts and reviewing how it responds. Some prompts are straightforward, while others are more complex or open-ended. Human reviewers evaluate the answers for clarity, relevance, tone, and whether the model is staying within its expected boundaries. Testing also looks at edge cases, such as vague or confusing inputs, to see how the model handles uncertainty.

This round of testing helps determine if the model is ready for deployment. It also identifies areas where further adjustment may be needed, whether through additional tuning, safety interventions, or prompt engineering.

8) Red Teaming

Once fine-tuning is complete, the model undergoes red teaming. This means that external experts test the model with the goal of finding weaknesses. They try to trick the model into producing biased, harmful, or otherwise problematic outputs. The goal here is to identify failure points — things the model may do that are unsafe or undesirable. This feedback is used to adjust the model or its safeguards before it goes live.

9) Deployment

Deployment means making the model available for use. It’s placed on servers where it can handle requests from users. These models are often accessed via APIs, so they can be used in a variety of applications like chatbots or virtual assistants. At this stage, the model is being actively used by real people, and it’s closely monitored to ensure it’s working as expected.

Figure 2 shows how a fine-tuned model, which is static (read-only) and stateless (no inherent memory), is deployed across multiple servers and data centers.

10) Additional Fine-Tuning

Even after deployment, fine-tuning doesn’t stop. Based on user feedback or new needs, the model may be further fine-tuned. This could be for new tasks, new domains of knowledge, or to improve performance in specific scenarios. Additional fine-tuning incrementally builds on the model’s existing capabilities and may impart updated knowledge, improvements to desired behavior, and stylistic tweaks, extending its life and usefulness without a fully pretraining a new model.

11) Retirement

Eventually, models are deprecated. This happens when a newer, more capable model is released, or when the model no longer meets performance or safety standards. Retiring a model doesn’t mean it’s deleted, but it’s no longer used in production systems. It may still be kept for internal purposes, used in research, or archived for historical purposes.

Conclusion

Of course, this overview leaves out many technical details, but the key steps are accurate. Large Language Models are designed, trained, evaluated, and deployed through a deliberate and repeatable process. Understanding that process is necessary if we want to reason clearly about what these systems are, what they can do, and where their limits are.